PBT assessment and prioritization by PBT Index and consensus modeling: Comparison of screening results from structural models

Gramatica, Paola; Cassani, Stefano; Sangion, Alessandro

doi:10.1016/j.envint.2014.12.012

Cited by 48 publications

(25 citation statements)

References 22 publications

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“…The Insubria graph of PBT model is given in Figure . It indicates the predicted PBT Index values from Equation 5 together with the hat values for the eight chemicals as well as the training set chemicals used for the generation of PBT Index model …”

Section: Resultsmentioning

confidence: 99%

“…Gramatica et al . developed Persistent Bioaccumulation and Toxicity (PBT) Index model to evaluate PBT behaviour of chemicals directly from their structural features.…”

Section: Methodsmentioning

confidence: 99%

“…The following PBT Index (eq. 5) model which was validated in line with the OECD principles for QSAR was used to assess the PBT behaviour of the eight pesticides.

true PBT Index = - 1.46 (\pm 0.19) + 0.64 (\pm 0.05) nX + 0.22 (\pm 0.02) nBondsM - 0.06 (\pm 0.06) MAXDP 2 - 0.39 (\pm 0.12) nHBDon_Lipinski

…”

Section: Methodsmentioning

confidence: 99%

“…PaDEL‐Descriptor software was used to calculate descriptors (nX, nBondsM, MAXDP2, and nHBDon_Lipinski) in the PBT Index model for the eight pesticides. The symbol, type, meaning and source of descriptors appearing PBT Index model were provided in Table S5 (Supporting Information).…”

Section: Methodsmentioning

confidence: 99%

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Assessments of Algal Toxicity and PBT Behaviour of Pesticides with No Eco‐toxicological Data: Predictive Ability of QSA/(T)R Models

Gökçe

Saçan

2019

Molecular Informatics

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This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement.Abstract: In the present study, the effect of eight pesticides with no ecotoxicological data on the growth rate of Chlorella vulgaris was measured. The selected pesticides are acetochlor, acetamiprid, boscalid diphenamid, gibberellic acid, ioxynil, diclofop and 2,4,5-T. The algal toxicity (IC 50 ) of boscalid could not be determined within its solubility limit. Acetamiprid, diphenamid and gibberellic acid revealed IC 50 values > 100 mg/L. Among the others, the order of 96-h IC 50 of pesticides was found as acetochlor > ioxynil > diclofop > 2,4,5-T. The IC 50 values were also predicted by using four Quantitative Structure-Activity/(Toxicity) Relationship (QSA/ (T)R) models selected from the literature. The predictions of the models provided by QSARINS-Chem module of QSARINS as well as those obtained in our previous studies were compared with the results of experimental algal toxicity tests that were performed in our laboratory. The QSTR model generated for the toxicity of diverse chemicals to freshwater algae was able to correctly predict the toxicity order of the pesticides tested in the present study, confirming the utility of the QSA/(T)R approach. Additionally, Persistence, Bioaccumulation and Toxicity (PBT) Index model provided via the software QSARINS was employed and boscalid and diclofop were found to be PBT chemicals based on the PBT model. The present study will be very helpful when a more holistic approach applied to understand the fate of these chemicals in the environment.

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Section: Resultsmentioning

confidence: 99%

“…Gramatica et al . developed Persistent Bioaccumulation and Toxicity (PBT) Index model to evaluate PBT behaviour of chemicals directly from their structural features.…”

Section: Methodsmentioning

confidence: 99%

“…The following PBT Index (eq. 5) model which was validated in line with the OECD principles for QSAR was used to assess the PBT behaviour of the eight pesticides.

true PBT Index = - 1.46 (\pm 0.19) + 0.64 (\pm 0.05) nX + 0.22 (\pm 0.02) nBondsM - 0.06 (\pm 0.06) MAXDP 2 - 0.39 (\pm 0.12) nHBDon_Lipinski

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Assessments of Algal Toxicity and PBT Behaviour of Pesticides with No Eco‐toxicological Data: Predictive Ability of QSA/(T)R Models

Gökçe

Saçan

2019

Molecular Informatics

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show abstract

“…For this, objective feature selection in QSARINS ver. 2.2.2 [35][36][37][38] was performed. As a rule, molecular descriptors with high co-linearity (|r|>0.90) and nearly constant (> 95%) were excluded.…”

Section: Molecular Descriptor Pruningmentioning

confidence: 99%

Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling

Masand¹,

Rastija

Patil

et al. 2020

SAR and QSAR in Environmental Research

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A quantitative structure-activity relationship (QSAR) model was built from a dataset of 54 peptide-type compounds as SARS-CoV inhibitors. The analysis was executed to identify prominent and hidden structural features that govern anti-SARS-CoV activity. The QSAR model was derived from the genetic algorithm-multi-linear regression (GA-MLR) methodology. This resulted in the generation of a statistically robust and highly predictive model. In addition, it satisfied the OECD principles for QSAR validation. The model was validated thoroughly and fulfilled the threshold values of a battery of statistical parameters (e.g. r 2 = 0.87, Q 2 loo = 0.82). The derived model is successful in identifying many atom-pairs as important structural features that govern the anti-SARS-CoV activity of peptide-type compounds. The newly developed model has a good balance of descriptive and statistical approaches. Consequently, the present work is useful for future modifications of peptide-type compounds for SARS-CoV and SARS-CoV-2 activity.

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Overview ofREACHRegulation and Its Implications for the Military Sector

Ferreira¹,

Freire²,

Ribeiro³

2019

Energetic Materials and Munitions

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PBT assessment and prioritization by PBT Index and consensus modeling: Comparison of screening results from structural models

Cited by 48 publications

References 22 publications

Assessments of Algal Toxicity and PBT Behaviour of Pesticides with No Eco‐toxicological Data: Predictive Ability of QSA/(T)R Models

Assessments of Algal Toxicity and PBT Behaviour of Pesticides with No Eco‐toxicological Data: Predictive Ability of QSA/(T)R Models

Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling

Overview ofREACHRegulation and Its Implications for the Military Sector

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